Imbalanced Hyperspectral Image Classification with an Adaptive Ensemble Method Based on SMOTE and Rotation Forest with Differentiated Sampling Rates

IEEE Geoscience and Remote Sensing Letters

Volumn 16, Issue 12, 2019, Pages 1879-1883

  Feng, Wei ^a,c   Huang, Wenjiang ^a   Bao, Wenxing ^b  

^a Chinese Academy of Sciences   (China)

^b BEIFANG UNIVERSITY OF NATIONALITIES   (China)

^c XIDIAN UNIVERSITY   (China)

Author keywords

Classification; ensemble learning; hyperspectral image; multiclass imbalance learning; SMOTE

Indexed keywords

DECISION TREES; HYPERSPECTRAL IMAGING; IMAGE CLASSIFICATION; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

CLASSIFICATION TASKS; DIFFERENTIATED SAMPLING RATES; ENSEMBLE CLASSIFIERS; ENSEMBLE LEARNING; HYPERSPECTRAL DATA CLASSIFICATION; MULTICLASS IMBALANCE LEARNING; SMOTE; TRADITIONAL CONSTRUCTIONS;

CLASSIFICATION (OF INFORMATION);

CONSTRUCTION METHOD; DATA SET; IMAGE CLASSIFICATION; MACHINE LEARNING; MULTISPECTRAL IMAGE; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; SAMPLING;

EID: 85075604307     PISSN: 1545598X     EISSN: 15580571     Source Type: Journal    
DOI: 10.1109/LGRS.2019.2913387     Document Type: Article

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